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Deep morphological recognition of kidney stones using intra-operative endoscopic digital videos
To this end, a computer-aided video classifier was developed to predict in-situ the morphology of stone using an intra-operative digital endoscopic video acquired in a clinical setting.
On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classification
Deep learning has shown great promise in diverse areas of computer vision, such as image classification, object detection and semantic segmentation, among many others.
On the in vivo recognition of kidney stones using machine learning
This pilot study compares the kidney stone recognition performances of six shallow machine learning methods and three deep-learning architectures which were tested with in-vivo images of the four most frequent urinary calculi types acquired with an endoscope during standard ureteroscopies.
Towards Automatic Recognition of Pure & Mixed Stones using Intraoperative Endoscopic Digital Images
A deep convolutional neural network (CNN) was trained to predict the composition of both pure and mixed stones.
Assessing deep learning methods for the identification of kidney stones in endoscopic images
Knowing the type (i. e., the biochemical composition) of kidney stones is crucial to prevent relapses with an appropriate treatment.
